- ml/Cargo.toml: describe why ndarray's blas feature stays disabled
(CI compile pool has no libopenblas-dev; GPU cuBLAS handles the
hot path) rather than labelling it a TODO.
- dbn_sequence_loader.rs: Wave C regime-detection branch emits zeros
when only that flag is enabled; the live feed lives under WaveD.
Reword from "TODO Wave C" to a description of that superseding.
- ensemble/adapters/{liquid,tggn,tlob,xlstm}.rs: checkpoint loading
currently constructs fresh GpuLinear weights and logs a runtime
warning so the ignored checkpoint path is visible. No new
functionality, just reword the repeated TODO.
- ensemble/model_adapter.rs: the neutral-prediction adapter is
guarded by the ensemble's confidence threshold (0.0 = filtered),
making it a no-op stub used for end-to-end wiring. Describe that
contract explicitly.
- hyperopt/adapters/tft.rs: the input_dim=51 line is load-bearing
(5 static + 10 known + 36 unknown matches the CUDA layout). Drop
the "should be 42" aside.
- trainers/tft/trainer.rs: initialize_optimizer returns Err until
GpuAdamW is wired; the `let _ = &self.optimizer;` anchor in the
training loop keeps the migration target visible.
- trainers/tlob.rs: save_checkpoint / serialize_model both surface
errors until GpuVarStore safetensors serialisation lands. Mark
the gap declaratively rather than as a TODO.
- transformers/mod.rs: only AttentionMask is implemented in the
attention submodule; drop the aspirational re-export list.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
File was 126 LOC of dangling test code referencing types that don't exist
(FeatureConfig, MarketTick, MarketMicrostructure, TradeFlowFeatures,
FinancialFeatureExtractor, percentile). Not declared via `mod features;`
in transformers/mod.rs, so the compiler never saw it. Pure dead weight.
HFTTransformerConfig had seven write-only bool flags with zero conditional
readers anywhere in the workspace:
- FFLAG-017: use_market_microstructure, use_order_book_features,
use_trade_flow_features
- FFLAG-016: use_flash_attention, use_sparse_attention, use_cuda_graphs,
enable_profiling
Each was declared + defaulted + asserted in tests, but no code branched on
them. Deleted all 7 fields + Default init + production()/benchmark() setters
+ test assertions. Per feedback_no_feature_flags.md these were aspirational
knobs — the features they nominally gated have no implementation.
- Remove ALL #[cfg(feature = "cuda")] guards (~400+ occurrences)
- Remove ALL #[cfg_attr(not(feature = "cuda"), ignore)] test annotations (~250)
- Make cuda default feature in 9 ML crates (ml, ml-core, ml-dqn, ml-ppo, etc.)
- Convert nvrtc JIT compilation to precompiled nvcc (searchsorted, prefix_sum)
- Move compile_ptx_for_device() to ml-core for shared access
- Delete dead CPU code: multi_step.rs, self_supervised_pretraining.rs,
training_guard_gpu_tests.rs, CPU PER buffer paths, CPU Q-diagnostics
- Replace unwrap_or(Device::Cpu) with hard errors everywhere
- Remove dead is_cuda() else branches in DQN/PPO/hyperopt trainers
- Change config defaults from "cpu" to "cuda" (rainbow, tlob, pipeline)
- Port IQL value network to GPU kernel (5 CUDA entry points)
- Port HER goal relabeling to GPU kernel (warp-per-sample)
- Wire DSR GPU-to-CPU sync in training loop
- cfg!(feature = "cuda") → true in inference_validator
Zero warnings, zero errors across entire workspace.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Cast input to weight dtype in DQN residual, rmsnorm, noisy_layers
- Set use_gpu=true in QNetworkConfig defaults and all config sites
- Resolve BF16 boundary mismatches in attention, curiosity, branching,
distributional_dueling across ml-dqn
- GPU-resident regime ops with BF16 boundary casts, eliminate .expect() in CUDA paths
- Eliminate all Device::Cpu fallbacks — GPU-only across 10 ML crates
- PPO: cast logits to F32 before softmax, cast batch tensors to training dtype
- Gradient collapse detection for RegimeConditionalDQN
- Wire halt_grad_collapse from CUDA guard kernel to halt training
- Dead neuron detection uses active network VarMap + squeeze factored readback
- Increment gradient_logging_step in GPU PER path
- Gradient collapse warmup guards use original buffer_size
- Cap training steps per epoch + tracing migration
- Replace Tensor::all() with sum_all() for pinned Candle compatibility
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Move 17 library crates into crates/, CLI binary into bin/fxt,
consolidate 10 test crates into testing/, split config crate
from deployment config files.
Root directory reduced from 38+ to ~17 directories.
All Cargo.toml paths and build.rs proto refs updated.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
